The Analysis and Prevent in Traffic Accidents Based on Bayesian Network

Abstract:

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The development of the city has led to the frequent occurrence of traffic accidents. Whether we can analyze those accidents which had happened correctly will directly affect the avoidance of future ones of the similar kind. In this paper, we will establish Bayesian Networks traffic accident analysis model by K2 algorithm, which can make accident probability prediction and accident diagnosis.K2 algorithm is known to all with high efficiency and accuracy, but it requires to obtain order first, so to get the reasonable node order, first use clustering algorithm to divide the nodes into groups, in groups the similarity is high with each other. The probability of parent child relationship is larger, then reorder the nodes in every group by the expert experience finally determine the node sequence. Base on this, we can find the system weak links and adopt corresponding effective measures.

Abstract: The most important and critical step to improve road traffic safety is prediction and identification of traffic accident black spot. A new prediction model of traffic accident black spots is proposed based on GA-BP neural network algorithm and rough set theory. First of all, the traffic accident statistics of Jinwei Road in Tianjin are analyzed. With consideration of static road conditions, the samples of road accident black spots are obtained by the GA-BP neural network algorithm. Furthermore, an effective road traffic accident black spot prediction model is established by utilizing rough set theory with consideration of the impact of real time dynamic conditions. Finally, a numerical example is illustrated. Experimental results show that the proposed model with the combination of these two theories can reduce the hybrid and burdensome amount of data, lower the false alarm rate and improve the forecasting accuracy of accident black spots.

Abstract: The external traffic environment has a big influence to the traffic safety during the area of traffic conflict place，and to analysis the relationship between the external traffic environment factors and driving safety is helpful to improve the traffic safety. The method of comprehensive analysis the historical data and expert survey data is used to explore this question. And at the same time, the collision risk prediction model during the traffic conflict place is built by the Bayesian network. According to the data analyzing, the node variable, the state of variable and the conditional probability table of this model is also built. Finally, the software of Hugin is used to deal with the posteriori probability of collision risk, and the result proved that this model can predict the collision risk accurately during the traffic conflict area, and the data analyzing showed that the factor of the driver's intention, the vehicle speed and the headway have a significance influence to the traffic safety.